Bibliometric Analysis on Smart Self-Healing Nanocoating for 316L Stainless Steel Biomedical Implants
Bibliographic record
Abstract
This study presents a bibliometric analysis of research on smart self-healing nanocoatings for 316L stainless steel biomedical implants between 2015 and 2025. The aim is to explore publication trends, identify leading contributors, and uncover gaps in knowledge within this emerging field. A total of 237 documents were collected from the Scopus database using a well-defined search strategy. Performance analysis and science mapping techniques were applied using VOSviewer, Bibliometrix, and supporting tools. The results show a consistent increase in publication volume, with a notable rise after 2020, suggesting growing interest in self-healing materials for biomedical applications. The most common document types are research articles (44.3%) and reviews (38%), with most publications falling under materials science, engineering, and chemistry. India and China lead in publication count, while countries like Canada and Australia demonstrate high average citation impact. Keywords like “corrosion,” “biocompatibility,” and “hydroxyapatite” dominate the field, while “self-healing” appears infrequently, indicating an underexplored area. Experimental focus remains largely on in vitro studies, with limited in vivo or simulation-based research. Most coatings are tested in lab settings, and only a few studies move toward biological or computational validations. This paper highlights the need for broader interdisciplinary efforts and deeper translation of lab findings into real biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.076 | 0.161 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".